Skip to main content
SpringerLink
Log in

Severity estimation of brainstem in dementia MR images using moth flame optimized segmentation and fused deep feature selection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Dementia is an irreversible chronic neuro-disorder. Prediction of preclinical stages variation in dementia disorder helps to delay the progression. This study attempts to differentiate the brainstem (BS) structures for normal and different demented stages. BS structure is interconnected with many brain cortical structures that help to provide valuable pathological information about atrophy. This work is carried out using ADNI public database. Initially, skull-stripped MR images are used to perform BS segmentation using moth flame optimization-based multilevel Tsallis entropy method. Architecture such as AlexNet, GoogleNet and SqueezeNet is considered to extract features. The fusion of these features provides optimal information about BS structures for preclinical stages. The performance of fused deep features is evaluated using heat map and occlusion sensitivity map. Further, combined feature selection is carried out to extract the most distinct feature set using mutual information, minimum redundancy maximal relevance and recursive feature elimination methods. Finally, the analysis of variance is used to evaluate inter and intra-class variation of the subject. Results indicate that the suggested approach could segment the BS prominently. The correlation value was found to be > 0.97 in all the considered stages. The heatmap and occlusion sensitivity show the fused deep features provide highly discriminative features. The statistical performance of this fused feature set of Normal (CN)/EMCI, CN/EMCI, CN/LMCI, CN/MCI, CN/AD, EMCI/MCI, EMCI/AD, MCI/LMCI, MCI/AD and LMCI/AD shows high significant variation (p < 0.0001). Consequently, this approach captures the complex preclinical stage variation effectively and suitable to reduce the misdiagnosis rates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets analysed in the proposed work are available in the following public domain resources: [https://adni.loni.usc.edu/data-samples/access-data/].

References

  1. Ge C, Qu Q, Yu-Hua GuI, Jakola AS (2019) Multi-stream multi-scale deep convolutional networks for Alzheimer ’s disease detection using MR images. Neurocomputing 350:60–69. https://doi.org/10.1016/j.neucom.2019.04.023

    Article  Google Scholar 

  2. Kaabouch G (2019) A deep learning approach for diagnosis of mild cognitive impairment based on MRI images. Brain Sci 9(9):217. https://doi.org/10.3390/brainsci9090217

    Article  Google Scholar 

  3. Bi X, Xu Q, Luo X, Sun Q, Wang Z (2018) Analysis of progression toward Alzheimer’s disease based on evolutionary weighted random support vector machine cluster. Front Neurosci 12:716. https://doi.org/10.3389/fnins.2018.00716

    Article  Google Scholar 

  4. Saad SHS, Alashwah MMA, Alsafa AA, Dawoud AM (2020) The role of brain structural magnetic resonance imaging in the assessment of hippocampal subfields in Alzheimer’s disease. Egypt J Radiol Nucl Med. https://doi.org/10.1186/s43055-020-00164-8

    Article  Google Scholar 

  5. Gorji HT, Kaabouch N (2019) A deep learning approach for diagnosis of mild cognitive impairment based on MRI images. Brain Sci 9(9):217. https://doi.org/10.3390/brainsci9090217

    Article  Google Scholar 

  6. Simic G, Stanic M, Mladinov N, Jovanov M, Kostovic I, Hof PR (2009) Does Alzheimer disease begin in brainstem? Neuropathol Appl Neurobiol 35(6):532–554. https://doi.org/10.1111/j.1365-2990.2009.01038.x

    Article  Google Scholar 

  7. Ji X, Wang H, Zhu M, He Y, Zhang H, Chen X, Gao W, Fu Y (2021) Brainstem atrophy in the early stage of Alzheimer’s disease: a voxel-based morphometry study. Brain Imaging Behav 15:49–59. https://doi.org/10.1007/s11682-019-00231-3

    Article  Google Scholar 

  8. Uematsu M, Nakamura A, Ebashi M, Hirokawa K, Takahashi R, Uchihara T (2018) Brainstem tau pathology in Alzheimer’s disease is characterized by increase of three repeat tau and independent of amyloid β. Acta Neuropathol. Commun 6(1):1. https://doi.org/10.1186/s40478-017-0501-1

    Article  Google Scholar 

  9. Rohini P, Sundar S, Ramakrishnan S (2020) Differentiation of early mild cognitive impairment in brainstem MR images using multifractal detrended moving average singularity spectral features. Biomed Signal Process Control 57:101780. https://doi.org/10.1016/j.bspc.2019.101780

    Article  Google Scholar 

  10. Ge C, Qu Q, Yu-Hua GuI, Jakola AS (2019) Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images. Neurocomputing 350:60–69. https://doi.org/10.1016/j.neucom.2019.04.023

    Article  Google Scholar 

  11. Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3):907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046

    Article  Google Scholar 

  12. Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging 32(7):913–923. https://doi.org/10.1016/j.mri.2014.03.010

    Article  Google Scholar 

  13. Preeti M, Manik S, Sanjeev KS (2021) A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.11.016

    Article  Google Scholar 

  14. Aziz MAE, Ewees AA, Hassanien AE (2017) Whale optimization Algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256. https://doi.org/10.1016/j.eswa.2017.04.023

    Article  Google Scholar 

  15. Debendra M, Ratnakar D, Banshidhar M (2020) Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach. Biomed Signal Process Control 59:101912. https://doi.org/10.1016/j.bspc.2020.101912

    Article  Google Scholar 

  16. Govindarajan S, Swaminathan R (2020) Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks. Appl Intell 51(5):2764–2775. https://doi.org/10.1007/s10489-020-01941-8

    Article  Google Scholar 

  17. Zhengying L, Hong H, Yule D, Guangyao S (2020) DLPNet: A deep manifold network for feature extraction of hyperspectral imagery. Neural Netw 129:7–18. https://doi.org/10.1016/j.neunet.2020.05.022

    Article  MATH  Google Scholar 

  18. Chen B, Li J, Guo X, Lu G (2019) DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays. Biomed Signal Process Control 53:101554. https://doi.org/10.1016/j.bspc.2019.04.031

    Article  Google Scholar 

  19. Boussaad Leila, Boucetta Aldjia (2022) Deep-learning based descriptors in application to aging problem in face recognition. J King Saud Univ - Comp Info Sci 34(6):2975–2981. https://doi.org/10.1016/j.jksuci.2020.10.002

    Article  Google Scholar 

  20. Toğaçar M, Ergen B, Cömert Z (2020) Classification of white blood cells using deep features obtained from convolutional neural network models based on the combination of feature selection methods. Appl Soft Comput 97:106810. https://doi.org/10.1016/j.asoc.2020.106810

    Article  Google Scholar 

  21. Demirhan A (2018) The effect of feature selection on multivariate pattern analysis of structural brain MR images. Physica Med 47:103–111. https://doi.org/10.1016/j.ejmp.2018.03.002

    Article  Google Scholar 

  22. Iglesias JE, Liu C-Y, Thompson PM, Zhuowen Tu (2011) Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 30(9):1617–1634. https://doi.org/10.1109/TMI.2011.2138152

    Article  Google Scholar 

  23. Azimbagirad M, Simozo FH, Senra Filho ACS, Murta JLO (2020) Tsallis-entropy segmentation through MRF and Alzheimer anatomic reference for brain magnetic resonance Parcellation. Magn Reson Imaging 65:136–145. https://doi.org/10.1016/j.mri.2019.11.002

    Article  Google Scholar 

  24. Kaur K, Singh U, Salgotra R (2020) An enhanced moth flame optimization. Neural Comput Appl 32:2315–2349. https://doi.org/10.1007/s00500-021-06560-0

    Article  Google Scholar 

  25. Billah M, Waheed S (2020) Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimed Tools Appl 79:23633–23643. https://doi.org/10.1007/s11042-020-09151-7

    Article  Google Scholar 

  26. Maltar J, Marković I, Petrović I (2020) Visual place recognition using directed acyclic graph association measures and mutual information-based feature selection. Rob Auton Syst 132:103598. https://doi.org/10.1016/j.robot.2020.103598

    Article  Google Scholar 

  27. Padmavthi K, Sri RKK (2017) Detection of bundle branch block using adaptive bacterial foraging optimization and neural network. Egypt Inform J 18(1):67–74. https://doi.org/10.1016/j.eij.2016.04.004

    Article  Google Scholar 

  28. Diego O, Salvador H, Erik C, Gonzalo P, Omar A, Jorge G (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180. https://doi.org/10.1016/j.eswa.2017.02.042

    Article  Google Scholar 

  29. Rallabandi VPS, Tulpule K, Gattu M (2020) Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer’s disease using structural MRI analysis. Inf Med Unlocked. https://doi.org/10.1016/j.imu.2020.100305

    Article  Google Scholar 

  30. Abbas A, AbdelsameaGaber MMMM (2021) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell 51:854–864. https://doi.org/10.1007/s10489-020-01829-7

    Article  Google Scholar 

Download references

Funding

Not applicable for the submitted work.

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, India

    Ahana Priyanka & Kavitha Ganesan

Authors
  1. Ahana Priyanka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kavitha Ganesan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ahana Priyanka or Kavitha Ganesan.

Ethics declarations

Conflict of interest

The author declares no conflict of interest exits in the submission of this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priyanka, A., Ganesan, K. Severity estimation of brainstem in dementia MR images using moth flame optimized segmentation and fused deep feature selection. Neural Comput & Applic 35, 9093–9104 (2023). https://doi.org/10.1007/s00521-022-08167-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08167-4

Keywords

Search

Navigation